The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

Gao, Yun; Wang, Songhan; Guan, Kaiyu; Wolanin, Aleksandra; You, Liangzhi; Ju, Weimin; Zhang, Yongguang

doi:10.3390/rs12071111

Cited by 23 publications

(4 citation statements)

References 95 publications

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“…EVI, building on NDVI, includes adjustments for atmospheric disturbances, land background signal, and vegetation structure. These indices are essential for comprehending ecosystem dynamics, tracking agricultural production trends, and understanding the impacts of climate change 46 – 48 .…”

Section: Methodsmentioning

confidence: 99%

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Lu,

Fu,

Tang

et al. 2024

Sci Rep

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Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

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Section: Methodsmentioning

confidence: 99%

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Lu,

Fu,

Tang

et al. 2024

Sci Rep

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“…Specifically, to evaluate the importance of a given predictor for a given model, the PFI method is based on the comparison between the performances obtained with the original dataset and those obtained with a dataset in which the values of the predictor of interest are randomly permuted. The permutation allows the random variation of the predictor while preserving the natural distribution of the values of the predictor itself (Gao et al, 2020). By measuring the reduction of the model performance, the relative importance of the predictor can be evaluated (Putin et al, 2016).…”

Section: Performance Assessment: Validation Routinesmentioning

confidence: 99%

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Amato

Fiorucci

Martino

et al. 2023

Bull Eng Geol Environ

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The use of Artificial Neural Network (ANN) approaches has gained a significant role over the last decade in the field of predicting the distribution of effects triggered by natural forcing, this being particularly relevant for the development of adequate risk mitigation strategies. Among the most critical features of these approaches, there are the accurate geolocation of the available data as well as their numerosity and spatial distribution. The use of an ANN has never been tested at a national scale in Italy, especially in estimating earthquake-triggered landslides susceptibility. Based on the statistics deductible from the most up-to-date national inventory of earthquake-induced ground effects, i.e. the CEDIT catalogue, it results that over 56% of the ground effects triggered by earthquakes in Italy are represented by landslides. Therefore, a landslide dataset with such high geolocation precision was suitable to evaluate the efficiency of an ANN to explain the distribution of landslides over the Italian territory. An ex-post evaluation of the ANN-based susceptibility model was also performed, using a sub-dataset of historical data with lower geolocation precision. The ANN training highly performed in terms of spatial prediction, by partitioning the Italian landscape into slope units.The obtained results returned a distribution of potentially unstable slope units with maximum concentrations primarily distributed in the central-northern Apennines and secondarily in the southern Apennines. Moreover, the Alpine sector clearly appeared to be divided into two areas, a western one with relatively low susceptibility to earthquake-triggered landslides and the eastern sector with a higher susceptibility. However, the scale of the analysis carried out to train the ANN does not allow it to be applied for planning purposes or for seismic microzonation studies, for which training on a smaller spatial scale will be required.

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“…Crop simulation models, also referred to as crop development models, predict yields by simulating the entire growth period of crops. These models incorporate physiological data and environmental factors, including climate and soil conditions [6]. Prominent examples in this category are the AFRCWHEAT2 and CERES models [7].…”

Section: Introductionmentioning

confidence: 99%

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

Meghraoui,

Sebari,

Pilz

et al. 2024

Technologies

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Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.

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The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

Cited by 23 publications

References 95 publications

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges

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